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Differentiate a circuit with respect to its inputs by
Inherits From: Differentiator
tfq.differentiators.LinearCombination(
weights, perturbations
)
linearly combining values obtained by evaluating the op using parameter values perturbed about their forwardpass values.
my_op = tfq.get_expectation_op()
weights = [5, 6, 7]
perturbations = [0, 0.5, 0.25]
linear_differentiator = tfq.differentiators.LinearCombination(
weights, perturbations
)
# Get an expectation op, with this differentiator attached.
op = linear_differentiator.generate_differentiable_op(
analytic_op=my_op
)
qubit = cirq.GridQubit(0, 0)
circuit = tfq.convert_to_tensor([
cirq.Circuit(cirq.X(qubit) ** sympy.Symbol('alpha'))
])
psums = tfq.convert_to_tensor([[cirq.Z(qubit)]])
symbol_values_array = np.array([[0.123]], dtype=np.float32)
# Calculate tfq gradient.
symbol_values_tensor = tf.convert_to_tensor(symbol_values_array)
with tf.GradientTape() as g:
g.watch(symbol_values_tensor)
expectations = op(circuit, ['alpha'], symbol_values_tensor, psums
)
# Gradient would be: 5 * f(x+0) + 6 * f(x+0.5) + 7 * f(x+0.25)
grads = g.gradient(expectations, symbol_values_tensor)
# Note: this gradient visn't correct in value, but showcases
# the principle of how gradients can be defined in a very flexible
# fashion.
grads
tf.Tensor([[5.089467]], shape=(1, 1), dtype=float32)
Args  

weights

Python list of real numbers representing linear
combination coeffecients for each perturbed function
evaluation.

perturbations

Python list of real numbers representing
perturbation values.

Methods
differentiate_analytic
@tf.function
differentiate_analytic( programs, symbol_names, symbol_values, pauli_sums, forward_pass_vals, grad )
differentiate_sampled
@tf.function
differentiate_sampled( programs, symbol_names, symbol_values, pauli_sums, num_samples, forward_pass_vals, grad )
generate_differentiable_op
generate_differentiable_op(
*, sampled_op=None, analytic_op=None
)
Generate a differentiable op by attaching self to an op.
This function returns a tf.function
that passes values through to
forward_op
during the forward pass and this differentiator (self
) to
backpropagate through the op during the backward pass. If sampled_op
is provided the differentiators differentiate_sampled
method will
be invoked (which requires sampled_op to be a sample based expectation
op with num_samples input tensor). If analytic_op is provided the
differentiators differentiate_analytic
method will be invoked (which
requires analytic_op to be an analytic based expectation op that does
NOT have num_samples as an input). If both sampled_op and analytic_op
are provided an exception will be raised.
This generate_differentiable_op()
can be called only ONCE because
of the one differentiator per op
policy. You need to call refresh()
to reuse this differentiator with another op.
Args  

sampled_op

A callable op that you want to make differentiable
using this differentiator's differentiate_sampled method.

analytic_op

A callable op that you want to make differentiable
using this differentiators differentiate_analytic method.

Returns  

A callable op that who's gradients are now registered to be
a call to this differentiators differentiate_* function.

refresh
refresh()
Refresh this differentiator in order to use it with other ops.